Is RSA effective for conflict modeling? Using Representation Similarity Analysis [1]} (see Sec. ) to model layer-wise conflict distribution, helps us in two ways: first, we obtain an ordering on the conflict likelihood of the layers, and second, we find the preferred task to bind with.
Without the conflict distribution, the space of possible bindings and layer expansions is too large.
To investigate the difficulty of finding an optimal shared network, we perform a random growth experiment with the Split-Cifar100 dataset as follows.
When a new task arrives, we bind it with a random existing task, randomly choose the number of layers to expand and a subset of layers to expand.
As shown in Fig REF , the network obtained through random growth performs much worse when compared to the network optimized using RSA.
In fact, the average gain values go down to -20 with random-growth as compared to positive values obtained with RSA.
We therefore conclude that RSA-based conflict distributions are crucial for efficient task binding and layer expansion in L2BG.
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